Question 1

Applying Statistical Inference: An Example If an experimenter finds that the difference between two treatment groups is significant at the .05 level, then
A)the odds of obtaining this difference by chance are 95 in 100 times.
B)the probability of this treatment effect is 5%.
C)we would expect chance to produce a result this large less than 5 in 100 times.
D)we should accept the null hypothesis.

Accepted Answer

The .05 level of significance means that there is a 5% chance that the observed difference between the treatment groups occurred by chance. Therefore, we would expect chance to produce a result this large less than 5 in 100 times. This means that it is unlikely that the observed difference is due to chance and suggests that the treatment effect is real. We should reject the null hypothesis.

Question 2

Applying Statistical Inference: An Example All of these illustrate a directional hypothesis except:
A)Behavior therapy combined with antidepressant medication produces greater improvement in depressive symptoms than behavior therapy
Alone.
B)EEG biofeedback combined with a traditional 12-step program produces a higher abstinence rate in alcoholics than a traditional 12-step program
Alone.
C)Students experience more colds during midterms than the rest of the semester.
D)The risk of heart attack differs for patients who exercise or who are sedentary.

Accepted Answer

A directional hypothesis specifies the direction of the expected difference or relationship (e.g., "greater than," "less than," "more than"). Choices A, B, and C all specify a direction of difference. However, D only states that there is a difference, without specifying the direction of the difference, making it non-directional.

Question 3

Applying Statistical Inference: An Example Which criterion does an experimenter use to decide whether to accept or reject the null hypothesis?
A)beta
B)effect size
C)power
D)significance level

Accepted Answer

The criterion an experimenter uses to decide whether to accept or reject the null hypothesis is the significance level, also known as alpha. The significance level represents the maximum probability of rejecting the null hypothesis when it is actually true. Commonly chosen values for alpha are 0.05 and 0.01, indicating that the experimenter is willing to accept a 5% or 1% chance of incorrectly rejecting the null hypothesis. If the p-value calculated from the data is less than the significance level, the null hypothesis is rejected as unlikely to be true.

Question 4

Weighing the Evidence As the variability in a dependent variable ____, it becomes ____ to reject the null hypothesis.
A)increases, easier
B)increases, harder
C)decreases, harder
D)none of these

Accepted Answer

As the variability in a dependent variable increases, it becomes harder to reject the null hypothesis because increased variability typically leads to larger standard errors, making it more difficult to find statistically significant differences.

Question 5

Weighing the Evidence The results of an experiment are statistically significant when
A)the difference between group scores exceeds normal variability.
B)the difference between treatment means is large.
C)they can be replicated.
D)variability in each treatment condition is high.

Accepted Answer

Statistically significant results indicate that the difference between group scores is unlikely due to chance or random variation. This typically occurs when the difference between treatment means exceeds the normal variability or noise in the data. The other options may be relevant to the validity and reliability of the experiment, but do not directly speak to statistical significance.

Question 6

Weighing the Evidence We never directly test ____ when we evaluate the results of an experiment. A)H₁ B)the alternative hypothesis C)the null hypothesis D)H₁or the alternative hypothesis

Accepted Answer

The answer of Weighing the Evidence We never directly test...

Question 7

Applying Statistical Inference: An Example Since side effects can threaten patient lives, the developer of a new antipsychotic drug wants to minimize the risk of Type 2 error when measuring liver damage.Which level of significance should she use?
A)p < .001
B)p < .01
C)p < .03
D)p < .05

Accepted Answer

The developer of the new antipsychotic drug wants to minimize the risk of Type 2 error when measuring liver damage. This means that she wants to minimize the probability of falsely accepting the null hypothesis that the drug does not cause liver damage, when it actually does. This is known as a type II error.

In order to minimize the risk of type II error, the level of significance (alpha) should be set at a lower value. A lower level of significance means that the null hypothesis will only be rejected if there is very strong evidence against it, which reduces the risk of falsely accepting it.

Out of the given options, D (p < .05) is the most commonly used level of significance in scientific research. It allows for a 5% chance of falsely rejecting the null hypothesis, which means that there is a 95% confidence that any observed effect is real and not due to chance alone. This level of significance should be sufficient for the developer to minimize the risk of type II error while still allowing for meaningful analysis of the data.

Question 8

Weighing the Evidence We are more likely to reject the null hypothesis when
A)the amount of variability in the population we sampled is low.
B)the experimental manipulation did not affect the dependent variable.
C)the sample has more variability than the population.
D)the test statistic is very small.

Accepted Answer

A low amount of variability in the population sampled increases the likelihood of finding a significant difference between groups, making it easier to reject the null hypothesis.

Question 9

Applying Statistical Inference: An Example A ____ is a set of scores that is symmetrical and bell-shaped.
A)frequency histogram
B)normal curve
C)sampling distribution
D)skewed distribution

Accepted Answer

A symmetrical and bell-shaped distribution is commonly known as a normal distribution, which is represented by a normal curve. Therefore, the best choice is B) normal curve.

Question 10

Applying Statistical Inference: An Example Where do most scores fall along a normal curve?
A)at the end with the longest tail
B)at the left end of the distribution
C)at the right end of the distribution
D)near the center

Accepted Answer

Most scores in a normal distribution fall near the center, which is also known as the mean of the distribution.

Question 11

Applying Statistical Inference: An Example We should select a significance level ____ in order to conduct a valid test of our hypothesis.
A)after we have collected all experimental data
B)after we have performed all statistical tests
C)based on our test statistic
D)before conducting the experiment

Accepted Answer

The significance level should be chosen before conducting the experiment to ensure that the test is properly planned and that the results are reliable. The significance level is a predetermined threshold for rejecting the null hypothesis, and it should be chosen based on considerations such as the consequences of a type I error (rejecting a true null hypothesis) and the desired level of precision in the experiment.

Question 12

Weighing the Evidence When we perform a statistical test, we evaluate
A)the internal validity of our experiment.
B)the power of our experiment.
C)whether the null hypothesis is a likely explanation for our results.
D)whether the research hypothesis is true.

Accepted Answer

When we perform a statistical test, we evaluate whether the null hypothesis (usually the opposite of our research hypothesis) is a likely explanation for our results. We cannot prove the truth of our research hypothesis, but we can reject the null hypothesis and provide evidence in support of our hypothesis. We are not directly evaluating the internal validity or power of the experiment, although these factors may influence the outcome of the test.

Question 13

Applying Statistical Inference: An Example Typically, researchers
A)can precisely measure the degree of overlap between treatment populations.
B)expect a high degree of overlap between treatment groups when manipulation of the independent variable is effective.
C)find it easier to reject the null hypothesis when the variability of the dependent variable in the sampled population is high.
D)expect some degree of overlap between treatment populations.

Accepted Answer

It is expected to have some degree of overlap between treatment populations, as this allows for the possibility of finding a significant difference between the groups through statistical inference. Without any overlap, it would be difficult to determine whether any observed differences are due to the independent variable or simply due to chance. Therefore, researchers typically expect and plan for some degree of overlap between treatment populations.

Question 14

Applying Statistical Inference: An Example When we set our significance level at .01, this corresponds to the notation,
A)p < .01.
B)p = .01.
C)p ≥ .01.
D)p > .01.

Accepted Answer

Setting the significance level at .01 means that we are willing to accept a maximum probability (p-value) of .01 for rejecting the null hypothesis. Therefore, if the p-value is less than .01, we can reject the null hypothesis at this level of significance. Thus, the correct notation is p < .01.

Question 15

Applying Statistical Inference: An Example Many statistical tests assume that the population you have sampled is normally distributed.This means that if you could measure everyone in the population on the dependent variable,
A)the bulk of the scores would fall above the mean.
B)the distribution of scores would be symmetrical and bell-shaped.
C)the left end of the distribution would have the longest tail.
D)the right end of the distribution would have the longest tail.

Accepted Answer

A normal distribution is characterized by a symmetrical and bell-shaped distribution of scores, with the majority of scores falling near the mean and fewer scores falling farther away from the mean towards the tails of the distribution.

Question 16

Applying Statistical Inference: An Example Which of these illustrates a directional hypothesis?
A)Cognitive Behavior Therapy is more effective in treating agoraphobia than psychoanalytic therapy.
B)Greek and independent student GPAs vary.
C)Men and women differ in manual dexterity.
D)Students who study together earn different test scores than students who study alone.

Accepted Answer

A directional hypothesis makes a prediction about the direction of the relationship between variables. In option A, the hypothesis predicts that one treatment (Cognitive Behaviour Therapy) will be more effective in treating agoraphobia than the other treatment (psychoanalytic therapy), indicating a directional relationship. The other options do not make a clear prediction about the direction of the relationship between variables, making them non-directional hypotheses.

Question 17

Weighing the Evidence Statistical tests allow us to
A)determine the exact odds that the independent variable is responsible for the differences we observe between treatment groups.
B)compare the differences between treatment groups to normal variability in the dependent variable.
C)estimate the risk of committing a Type 2 error.
D)prove that the research hypothesis is true.

Accepted Answer

Statistical tests allow us to compare the differences between treatment groups to normal variability in the dependent variable. They help us determine if the differences between groups are significant enough to conclude that the independent variable is responsible for those differences, or if they could have occurred due to chance. However, statistical tests cannot prove that the research hypothesis is true, as this would require additional evidence and confirmation from multiple studies.

Question 18

Applying Statistical Inference: An Example Mercedes will reject the null hypothesis if she observes a difference between treatment conditions that is so large that it could have occurred by chance less than 1 in 100 times.The .01 value is Mercede's
A)effect size.
B)risk of committing a Type 2 error.
C)significance level.
D)statistical power.

Accepted Answer

The significance level, represented by the .01 value, is the threshold at which Mercedes will reject the null hypothesis. This is the level of risk she is willing to take for committing a Type 1 error (rejecting the null hypothesis when it is actually true).

Question 19

Applying Statistical Inference: An Example Calvin has set his alpha level at .05.He will reject the null hypothesis if he observes a difference between treatment conditions that is so large that it could have occurred by chance
A)5 in 100 times.
B)at least 5 in 100 times.
C)less than 5 in 100 times.
D)more than 5 in 100 times.

Accepted Answer

Setting the alpha level at .05 means that Calvin is willing to accept a 5% chance of incorrectly rejecting the null hypothesis (Type I error). Therefore, he will reject the null hypothesis if he observes a difference between treatment conditions that could have occurred by chance less than 5 in 100 times. This corresponds to choice C.

Question 20

Applying Statistical Inference: An Example Variability can be produced by experimental errors, which are fluctuations in subjects' scores produced by
A)experimenter bias.
B)extraneous variables in the experimental procedure.
C)influences on subjects not related to the independent variable.
D)all of these

Accepted Answer

Experimental errors can be caused by any of the three options given (experimenter bias, extraneous variables, and influences on subjects not related to the independent variable), therefore the best choice is D, which includes all of the options.

Quiz 13: Why We Need Statistics

Applying Statistical Inference: An Example If an experimenter finds that the difference between two treatment groups is significant at the .05 level, then

Applying Statistical Inference: An Example All of these illustrate a directional hypothesis except:

Applying Statistical Inference: An Example Which criterion does an experimenter use to decide whether to accept or reject the null hypothesis?

Weighing the Evidence As the variability in a dependent variable , it becomes to reject the null hypothesis.

Weighing the Evidence The results of an experiment are statistically significant when

Weighing the Evidence We never directly test ____ when we evaluate the results of an experiment.

Applying Statistical Inference: An Example Since side effects can threaten patient lives, the developer of a new antipsychotic drug wants to minimize the risk of Type 2 error when measuring liver damage.Which level of significance should she use?

Weighing the Evidence We are more likely to reject the null hypothesis when

Applying Statistical Inference: An Example A ____ is a set of scores that is symmetrical and bell-shaped.

Applying Statistical Inference: An Example Where do most scores fall along a normal curve?

Applying Statistical Inference: An Example We should select a significance level ____ in order to conduct a valid test of our hypothesis.

Weighing the Evidence When we perform a statistical test, we evaluate

Applying Statistical Inference: An Example Typically, researchers

Applying Statistical Inference: An Example When we set our significance level at .01, this corresponds to the notation,

Applying Statistical Inference: An Example Many statistical tests assume that the population you have sampled is normally distributed.This means that if you could measure everyone in the population on the dependent variable,

Applying Statistical Inference: An Example Which of these illustrates a directional hypothesis?

Weighing the Evidence Statistical tests allow us to

Applying Statistical Inference: An Example Mercedes will reject the null hypothesis if she observes a difference between treatment conditions that is so large that it could have occurred by chance less than 1 in 100 times.The .01 value is Mercede's

Applying Statistical Inference: An Example Calvin has set his alpha level at .05.He will reject the null hypothesis if he observes a difference between treatment conditions that is so large that it could have occurred by chance

Applying Statistical Inference: An Example Variability can be produced by experimental errors, which are fluctuations in subjects' scores produced by